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Playlist Generation via Vector Representation of Songs

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Advances in Big Data (INNS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

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Abstract

This study proposes a song recommender system. The architecture is based on a distributed scalable big data framework. The recommender system analyzes songs a person listens to most and recommends a list of songs as a playlist. To realize the system, we use Word2vec algorithm by creating vector representations of songs. Word2vec algorithm is adapted to Apache Spark big data framework and run on distributed vector representation of songs to produce a playlist reflecting a person’s personal tastes. The performance results are evaluated in terms of hit rates at the end of the paper.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Kocaeli University, 41380, Izmit, Turkey

    Burak Köse, Süleyman Eken & Ahmet Sayar

Authors
  1. Burak Köse
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  2. Süleyman Eken
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  3. Ahmet Sayar
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Corresponding author

Correspondence to Süleyman Eken .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Köse, B., Eken, S., Sayar, A. (2017). Playlist Generation via Vector Representation of Songs. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

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